基于NSSAE的批次发酵过程质量相关与质量无关故障检测与诊断
TS251.1; 为了解决批次发酵过程中质量无关故障所可能引起的不必要停机,本文提出了噪声半监督堆叠自编码器(Noised semi-supervised stacked auto-encoder,NSSAE)算法以区分质量相关与质量无关故障.首先,基于互信息计算过程变量与质量变量间互信息,并对数据加入噪声以提高算法对质量相关信息挖掘能力.其次,构建NSSAE的过程监测模型,在模型的首层自编码器和最后一层自编码器中构建故障检测和质量相关检测指标,并利用核密度估计计算对应的控制极限.最后,利用深度重构贡献图(Deep reconstruction-based contribution,DRBC)...
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| Published in | 食品工业科技 Vol. 46; no. 3; pp. 1 - 10 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
江南大学物联网工程学院,江苏无锡 214122
01.02.2025
江南大学食品学院,江苏无锡 214122%香港科技大学化学与生物工程系,中国香港 999077%江南大学物联网工程学院,江苏无锡 214122%江南大学食品学院,江苏无锡 214122 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1002-0306 |
| DOI | 10.13386/j.issn1002-0306.2024020300 |
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| Abstract | TS251.1; 为了解决批次发酵过程中质量无关故障所可能引起的不必要停机,本文提出了噪声半监督堆叠自编码器(Noised semi-supervised stacked auto-encoder,NSSAE)算法以区分质量相关与质量无关故障.首先,基于互信息计算过程变量与质量变量间互信息,并对数据加入噪声以提高算法对质量相关信息挖掘能力.其次,构建NSSAE的过程监测模型,在模型的首层自编码器和最后一层自编码器中构建故障检测和质量相关检测指标,并利用核密度估计计算对应的控制极限.最后,利用深度重构贡献图(Deep reconstruction-based contribution,DRBC)定位故障根源.从数值仿真和乳酸菌批次发酵实验结果可知,本文提出的NSSAE算法能够准确区分质量相关与无关故障,首层的残差空间的检测指标的故障检测率接近100％,最后一层隐空间的检测指标能够准确识别质量相关故障和质量无关故障.基于DRBC诊断方法能在故障发生后准确识别发生故障的变量,该研究结果为批次发酵过程质量相关与质量无关故障监测问题提出了一种切实可行的过程监测方法. |
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| AbstractList | TS251.1; 为了解决批次发酵过程中质量无关故障所可能引起的不必要停机,本文提出了噪声半监督堆叠自编码器(Noised semi-supervised stacked auto-encoder,NSSAE)算法以区分质量相关与质量无关故障.首先,基于互信息计算过程变量与质量变量间互信息,并对数据加入噪声以提高算法对质量相关信息挖掘能力.其次,构建NSSAE的过程监测模型,在模型的首层自编码器和最后一层自编码器中构建故障检测和质量相关检测指标,并利用核密度估计计算对应的控制极限.最后,利用深度重构贡献图(Deep reconstruction-based contribution,DRBC)定位故障根源.从数值仿真和乳酸菌批次发酵实验结果可知,本文提出的NSSAE算法能够准确区分质量相关与无关故障,首层的残差空间的检测指标的故障检测率接近100％,最后一层隐空间的检测指标能够准确识别质量相关故障和质量无关故障.基于DRBC诊断方法能在故障发生后准确识别发生故障的变量,该研究结果为批次发酵过程质量相关与质量无关故障监测问题提出了一种切实可行的过程监测方法. |
| Abstract_FL | To address potential unnecessary shutdowns caused by quality-unrelated faults during batch fermentation processes,the paper proposed a noise semi-supervised stacked auto-encoder(NSSAE)to differentiate the quality-relevant and the quality-irrelevant faults.First,mutual information was applied to calculate the contribution from the process variables to quality variables,where artificial noised was introduced to enhance the performance.Second,an NSSAE-based monitoring model was established,wherein indicators for faults and quality variations were separately constructed from the first layer and the last layer of the model.Upon which,kernel density estimation was used to calculate the thresholds for the indicators.Lastly,deep reconstruction-based contribution was used to locate the root cause.Based on the results of numerical simulations and lactic acid bacteria batch fermentation experiments,the NSSAE algorithm proposed in this paper demonstrated the ability to accurately distinguish between quality-related and quality-irrelevant faults.The fault detection rate using the detection index of the first layer of residual space approached 100％.Moreover,the detection index in the final layer of latent space could precisely identify both quality-related and quality-irrelevant faults.Utilizing the DRBC diagnostic method,the specific variable causing the fault can be accurately pinpointed post-fault occurrence.These findings suggest a practical and effective process monitoring method for addressing quality-related and quality-irrelevant fault monitoring issues in the batch fermentation process. |
| Author | 章政 刘忠 朱金林 楼旭阳 |
| AuthorAffiliation | 江南大学物联网工程学院,江苏无锡 214122;江南大学食品学院,江苏无锡 214122%香港科技大学化学与生物工程系,中国香港 999077%江南大学物联网工程学院,江苏无锡 214122%江南大学食品学院,江苏无锡 214122 |
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| Author_FL | ZHANG Zheng LIU Zhong ZHU Jinlin LOU Xuyang |
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| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
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| DocumentTitle_FL | Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE |
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| Keywords | 质量相关故障 batch fermentation process quality-related faults 批次发酵过程 噪声半监督堆叠自编码器 fault detection and diagnosis 深度重构贡献图 deep reconstruction-based contribution 故障检测与诊断 noised semi-supervised stacked auto-encoder |
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| PublicationTitle | 食品工业科技 |
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| Publisher | 江南大学物联网工程学院,江苏无锡 214122 江南大学食品学院,江苏无锡 214122%香港科技大学化学与生物工程系,中国香港 999077%江南大学物联网工程学院,江苏无锡 214122%江南大学食品学院,江苏无锡 214122 |
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| Title | 基于NSSAE的批次发酵过程质量相关与质量无关故障检测与诊断 |
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